Content-boosted collaborative filtering for improved recommendations
P. Melville, R. Mooney, and R. Nagarajan. Eighteenth national conference on Artificial intelligence, page 187--192. Menlo Park, CA, USA, American Association for Artificial Intelligence, (2002)
Abstract
Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, &\#60;i&\#62;Content-Boosted Collaborative Filtering&\#60;/i&\#62;, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.
%0 Conference Paper
%1 citeulike:3839454
%A Melville, Prem
%A Mooney, Raymod J.
%A Nagarajan, Ramadass
%B Eighteenth national conference on Artificial intelligence
%C Menlo Park, CA, USA
%D 2002
%I American Association for Artificial Intelligence
%K collaborative-filtering, hybrid-recommendation
%P 187--192
%T Content-boosted collaborative filtering for improved recommendations
%U http://portal.acm.org/citation.cfm?id=777092.777124
%X Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, &\#60;i&\#62;Content-Boosted Collaborative Filtering&\#60;/i&\#62;, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.
%@ 0-262-51129-0
@inproceedings{citeulike:3839454,
abstract = {{Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, \&\#60;i\&\#62;Content-Boosted Collaborative Filtering\&\#60;/i\&\#62;, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.}},
added-at = {2017-11-15T17:02:25.000+0100},
address = {Menlo Park, CA, USA},
author = {Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass},
biburl = {https://www.bibsonomy.org/bibtex/2ef072ab7d6dfb0dba82d14a826bb86b8/brusilovsky},
booktitle = {Eighteenth national conference on Artificial intelligence},
citeulike-article-id = {3839454},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=777092.777124},
interhash = {985028099c1a29f116ad7434005895ac},
intrahash = {ef072ab7d6dfb0dba82d14a826bb86b8},
isbn = {0-262-51129-0},
keywords = {collaborative-filtering, hybrid-recommendation},
location = {Edmonton, Alberta, Canada},
pages = {187--192},
posted-at = {2009-07-28 17:17:57},
priority = {0},
publisher = {American Association for Artificial Intelligence},
timestamp = {2017-11-15T17:02:25.000+0100},
title = {{Content-boosted collaborative filtering for improved recommendations}},
url = {http://portal.acm.org/citation.cfm?id=777092.777124},
year = 2002
}